library(tidyverse)
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## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0
## ── Conflicts ───────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(maps)
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## Attaching package: 'maps'
## The following object is masked from 'package:purrr':
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## map
library(ggplot2)
library(viridis)
## Loading required package: viridisLite
library(ggthemes)
library(plotly)
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## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
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## filter
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## layout
library(gganimate)
library(countrycode)
happiness_2015 <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/2015.csv?token=ANDSWNMB7AQIJ672ORMVKL26OJ3H6")
happiness_2016 <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/2016.csv?token=ANDSWNPLYRU2KWDDIGFKSCS6OJ3JU")
happiness_2017 <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/2017.csv?token=ANDSWNKJG7RFYHRUAMITURC6OJ3M4")
happiness_2018 <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/2018.csv?token=ANDSWNNU3I7TF5JQKPQXAJK6OJ3OU")
happiness_2019 <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/2019.csv?token=ANDSWNNCDOIVOFWIVMMOOP26OJ3PU")
mental_health_facilities <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/mental_health_facilities.csv?token=ANDSWNMOS4SBJG3DTIMUUYK6OJ3QM")
suicide_death_rates <- read.csv("https://raw.githubusercontent.com/Reed-Statistics/math241S20PostGrp4/master/suicide-death-rates.csv?token=ANDSWNLHXALZJ4XT3PTCI6S6OJ3SU")
lower_bound <- rep(NA, 158)
upper_bound <- rep(NA, 158)
happiness_2015 <- happiness_2015 %>%
mutate(year = 2015) %>%
select(1, 3, 4, 6:11, 13) %>%
mutate(lower_bound = lower_bound, upper_bound = upper_bound) %>%
rename(country = 1,
happiness_rank = 2,
happiness_score = 3,
gdp = 4,
family = 5,
life_expectancy = 6,
freedom = 7,
trust_corruption = 8,
generosity = 9,
year = 10,
lower_whisker = 11,
upper_whisker = 12)
happiness_2016 <- happiness_2016 %>%
mutate(year = 2016) %>%
select(1, 3, 4:12, 14) %>%
rename(country = 1,
happiness_rank = 2,
happiness_score = 3,
gdp = 6,
family = 7,
life_expectancy = 8,
freedom = 9,
trust_corruption = 10,
generosity = 11,
year = 12,
lower_whisker = 4,
upper_whisker = 5)
happiness_2017 <- happiness_2017 %>%
mutate(year = 2017) %>%
select(-12) %>%
rename(country = 1,
happiness_rank = 2,
happiness_score = 3,
gdp = 6,
family = 7,
life_expectancy = 8,
freedom = 9,
trust_corruption = 10,
generosity = 11,
year = 12,
lower_whisker = 4,
upper_whisker = 5)
lower_bound <- rep(NA, 156)
upper_bound <- rep(NA, 156)
happiness_2018 <- happiness_2018 %>%
mutate(year = 2018) %>%
mutate(lower_bound = lower_bound, upper_bound = upper_bound) %>%
rename(country = 2,
happiness_rank = 1,
happiness_score = 3,
gdp = 4,
family = 5,
life_expectancy = 6,
freedom = 7,
trust_corruption = 9,
generosity = 8,
year = 10,
lower_whisker = 11,
upper_whisker = 12)
happiness_2019 <- happiness_2019 %>%
mutate(year = 2019) %>%
mutate(lower_bound = lower_bound, upper_bound = upper_bound) %>%
rename(country = 2,
happiness_rank = 1,
happiness_score = 3,
gdp = 4,
family = 5,
life_expectancy = 6,
freedom = 7,
trust_corruption = 9,
generosity = 8,
year = 10,
lower_whisker = 11,
upper_whisker = 12)
happiness <- do.call("rbind", list(happiness_2015, happiness_2016, happiness_2017, happiness_2018, happiness_2019))
suicide_death_rates <- suicide_death_rates %>%
select(-2) %>%
rename(sdr = 3)
countries <- happiness %>%
left_join(mental_health_facilities, by = c("country" = "Country")) %>%
left_join(suicide_death_rates, by = c(c("country" = "Entity"),
c("year" = "Year"))) %>%
select(-13)
## Warning: Column `country`/`Country` joining factors with different levels,
## coercing to character vector
## Warning: Column `country`/`Entity` joining character vector and factor, coercing
## into character vector
# retrieve the world map data
world_map <- map_data("world")
# merge map and countries data
countries_map <- left_join(countries, world_map, by = c("country" = "region"))
# create the static map colored with happiness scores
happiness_map <- ggplot(countries_map, aes(x = long, y = lat, group = group)) +
geom_polygon(aes(fill = happiness_score), colour = "white") +
scale_fill_viridis(direction = 1, option = "B") +
theme_void() +
theme(legend.direction = "vertical",
legend.title.align = 0,
legend.key.size = unit(0.5, "cm"),
legend.title=element_text(size = 10),
legend.text=element_text(size = 6)) +
labs(fill = "happiness score")
# create an animated map (unfinished, have to think about how to handle missing values)
# maybe add more years???
happiness_map +
transition_manual(year)
## nframes and fps adjusted to match transition
countries$continent <- countrycode(sourcevar = countries[, "country"],
origin = "country.name",
destination = "continent")
## Warning in countrycode(sourcevar = countries[, "country"], origin = "country.name", : Some values were not matched unambiguously: Kosovo
# manually assign the continent name to Kosovo
countries[countries$country =="Kosovo", "continent"] <- "Europe"
# create a scatterplot to visualize the relationship between happiness and suicide
suicide_happiness <- countries %>% ggplot(aes(x = happiness_score, y = sdr,
color = continent, frame = year, ids = country)) +
geom_point(aes(size = life_expectancy), alpha = 0.7) +
scale_x_log10() +
labs(x = "Happiness Score", y = "Suicide death rate per 100,000",
size = NULL, color = "Continent") +
theme_minimal()
ggplotly(suicide_happiness)